Signal denoising for thermal acoustic imaging

Deepika Aggrawal; Md Tarikul Islam; Mohsin Zafar; Dan Schonfeld; Kamran Avanaki

doi:10.1117/12.3003567

12 March 2024 Signal denoising for thermal acoustic imaging

Deepika Aggrawal, Md Tarikul Islam, Mohsin Zafar, Dan Schonfeld, Kamran Avanaki

Proceedings Volume 12842, Photons Plus Ultrasound: Imaging and Sensing 2024; 128420Z (2024) https://doi.org/10.1117/12.3003567
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Thermoacoustic imaging (TAI) combines microwave energy's penetration depth with ultrasound's spatial resolution for medical imaging. Denoising is crucial in TAI to refine low energy thermoacoustic signals, overcoming depth limitations and improving imaging precision. We utilized the MI-TAT system to capture signals from different phantoms and gather data for training and validation. Our architectural approach harnesses both time and spatial signal features, enabling the design of an advanced deep-learning model.

Citation Download Citation

Deepika Aggrawal, Md Tarikul Islam, Mohsin Zafar, Dan Schonfeld, and Kamran Avanaki "Signal denoising for thermal acoustic imaging", Proc. SPIE 12842, Photons Plus Ultrasound: Imaging and Sensing 2024, 128420Z (12 March 2024); https://doi.org/10.1117/12.3003567

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